Methods and apparatus to adjust content presented to an individual

ABSTRACT

Methods, apparatus, systems and articles of manufacture to adjust content presented to an individual are disclosed. An example system includes a first modality sensor to measure a first response of an individual to first content during a first time frame and a second modality sensor to measure a second response of the individual to the first content during the first time frame. The first modality sensor is to measure a third response of the individual to first content during a second time frame, and the second modality sensor is to measure a fourth response of the individual to the first content during the second time frame. The example system also includes a mental classifier executing instructions to determine a first mental classification of the individual based on a first comparison of the first response to a first threshold and a second comparison of the second response to a second threshold. The mental classifier also is to determine a second mental classification of the individual based on a third comparison of the third response to a third threshold and a fourth comparison of the fourth response to a fourth threshold. In addition, the mental classified is to determine a mental state of the individual based on a degree of similarity between the first mental classification and the second mental classification. The example system also includes a content modifier to at least one of modify the first content to include second content or replace the first content with second content based on the mental state.

RELATED APPLICATIONS

This patent arises from a continuation of U.S. application Ser. No.15/908,436 (now U.S. patent Ser. No. 10/771,844), titled “METHODS ANDAPPARATUS TO ADJUST CONTENT PRESENTED TO AN INDIVIDUAL,” and filed Feb.28, 2018, which is a continuation of U.S. patent application Ser. No.15/155,543 (now U.S. Pat. No. 9,936,250), titled “METHODS AND APPARATUSTO ADJUST CONTENT PRESENTED TO AN INDIVIDUAL,” and filed on May 16,2016, which claims priority to U.S. Provisional Application No.62/163,874, titled “MULTI-PHASIC EMOTION AND COGNITION CLASSIFIERS,”filed on May 19, 2015, and to U.S. Provisional Application No.62/272,423, titled “METHODS AND APPARATUS TO ADJUST CONTENT PRESENTED TOAN INDIVIDUAL,” filed on Dec. 29, 2015. U.S. application Ser. No.15/908,436; U.S. patent application Ser. No. 15/155,543; U.S.Provisional Application No. 62/163,874; and U.S. Provisional ApplicationNo. 62/272,423 are hereby incorporated herein by reference in theirentireties. Priority to U.S. application Ser. No. 15/908,436; U.S.patent application Ser. No. 15/155,543; U.S. Provisional Application No.62/163,874; and U.S. Provisional Application No. 62/272,423 is herebyclaimed.

FIELD OF THE DISCLOSURE

This disclosure relates generally to presenting content to an individualand, more particularly, to methods and apparatus to adjust contentpresented to an individual.

BACKGROUND

Individuals are exposed to multiple passive and interactive audio,visual, and audio-visual media content every day. The media contentproduces biologically based responses in the user that can be measuredby one or more sensors. An individual's biological and/or physicalresponse to an image can indicate emotional and cognitive responses.Personal logs and self-reporting of responses are often inaccurate andinclude biases due to human input. Additionally, personal logs andself-reporting rely on an accurate account by the individual of theirown emotional and cognitive reaction.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of an example system in which anexample content presentation device constructed in accordance with theteachings of this disclosure is implemented.

FIG. 2 is a block diagram of an example system that may be used toimplement one or more of the example devices of the example system ofFIG. 1.

FIG. 3 is a representation of an example mental classification grid thatmay be used by the example content presentation device of FIGS. 1 and/or2 to adjust content presented to an individual.

FIG. 4 is a representation of an example mental classification matrixthat may be used by the example content presentation device of FIGS. 1and/or 2 to adjust content presented to an individual.

FIG. 5 is a representation of overlapping time windows that may be usedby the example content presentation device of FIGS. 1 and/or 2 to adjustcontent presented to an individual.

FIG. 6 is a flowchart representative of example machine-readableinstructions for adjusting content presented to an individual that maybe executed by the example systems of FIGS. 1 and/or 2.

FIG. 7 is a flowchart representative of example machine-readableinstructions for adjusting a baseline and/or threshold that may beexecuted by the example systems of FIGS. 1 and/or 2.

FIG. 8 is a block diagram of an example processor platform structured toexecute the example machine-readable instructions of FIGS. 6 and 7implemented by the example systems of FIGS. 1 and/or 2.

The figures are not to scale. Wherever possible, the same referencenumbers will be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

Many different kinds of media content, such as audio, visual, andaudio-visual content are presented to individuals every day. Thepresentation of media content to the individual can result in abiologically based response in the individual, which can be used todetermine a mental state of the user. For example, a user may befrustrated or confused by media content displayed by a website,including the website interface, a game, etc. Data related to aparticular determined mental state of a user may be used, for example,in marketing applications. For example, if a user exhibits a biologicalresponse indicative of frustration caused by a website design, the usermay be less likely spend time on the website and purchase products fromthe business owner of the website. Thus, market researchers could usethe information about determined mental states of users to adapt thewebsite to provide a more enjoyable experience to a user and potentialconsumer as indicated by positive mental or emotional states detectedthrough the biological responses. Users having enjoyable and pleasantexperiences are more likely to spend more time on a website and may bemore likely to purchase products and/or services. In some examples, themedia content (e.g., the website) may adapt automatically based on thedetermined mental state of the individual.

Example apparatus and methods described herein adjust content (e.g.,media content such as commercial advertisements, websites, videos,Internet content, etc.) presented to an individual based on a determinedmental state (e.g., frustration, concentration, boredom, etc.) of theindividual. The mental state of the individual is determined based onmeasured responses (e.g., biometric responses such as heart rate, pupildilation, etc.) to presented content. In some examples, the responses ofthe individual are measured using modality sensors (e.g., sensors tomeasure biometric responses such as heart rate sensors, pressuresensors, facial expression detectors or facial action coding (FAC),etc.) and compared to respective thresholds to determine responseclassifications (e.g., high or low heart rate; high, medium, or lowpupil dilation; etc.).

In some examples, the thresholds may correspond to a baseline (e.g., athreshold amount above a baseline) associated with the response of theindividual over a period of time during presentation of the content. Forexample, the measured response of an individual over time may be used todevelop a baseline of biometric response activity. In such examples, anindividual may have biometric data recorded while exposed to neutral orbackground media (as opposed to a targeted media or stimulus). Thebaseline determines the level of biometric activity of the person in,for example, an inactive or uninvolved state. This determination of thebaseline may be different from individual to individual because, forexample, some individuals have higher resting heart rates than others ordifferent rates of respiration, etc. A threshold is set to indicatewhen, for example, an individual response indicative of excitement ishigh enough relative to the baseline to register as a positive response.Thus, the biometric data may be compared to the individualizedthreshold(s) to determine a high or low classification on amoment-by-moment basis for each subject, panelist, or user. Furthermore,in this example, the baseline is used as a reference for the threshold.If the comparison of the signal (the biometric data) to the threshold isconstantly resulting in a “low” or “high” classification, for example,the baseline and, in some instance also the threshold, are adjustedbecause the response of the individual has changed over time and is notbeing accurately represented or detected by the constantly “low” or“high” classification. In other words, the fluctuations or changes inthe individual's response may not be detected because the measuredresponses consistently remain below the threshold and, therefore, thethreshold and baseline are to be changed to capture the fluctuations orchanges.

In another example in which the baseline is adjusted based on themeasured responses, a baseline is increased when a measured response isconsistently higher than the threshold because this indicates that theresponse of the individual has changed. In this example, the baseline isincreased after a period of time (e.g., thirty seconds) to account forthe shift in the response of the individual (i.e., the fluctuations ofthe response of the individual are better measured if the baseline andthreshold are increased). For example, if an individual has a restingheart rate of 70 beats per minute (bpm) (e.g., a baseline measurement),the threshold for a “high” heart rate classification may be 75 bpm. Ifthe individual is frustrated, the heart rate may rise by, for example,approximately 10 bpm and, thus, be consistently classified as “high.” Inthis example, when the baseline is held at 70 bpm and the threshold isheld at 75 bpm, the fluctuations of the heart rate between 77 bpm and 85bpm are not detected because the entire signal between 75 bpm and 85 bpmis registered as “high.” Thus, changing the baseline and the thresholdto more accurately represent the new baseline heart rate of theindividual enables the fluctuations at the higher heart rate range to beclassified as high or low with respect to the new baseline andthreshold.

Additionally or alternatively, the baseline and/or threshold may bechanged based on the task being performed by the individual. Forexample, a simple task (e.g., shopping for a toothbrush) may result inlittle or no change to the baseline and/or threshold, but a more complextask (e.g., configuring a car) may result in a larger change to thebaseline and/or threshold. In some examples, the length of time the taskis estimated to take may affect the adjustment of the baseline and/orthreshold.

The response classifications measured during a first time frame may becombined to determine a mental classification (e.g., concentration,frustration, confusion, etc.). For example, the response classificationsof high GSR, high pupil dilation, high negative FAC, and low positiveFAC may indicate, when combined, that the individual is actively engagedand has a mental classification of frustration. In some examples, themental classification is determined by, for example, combining responseclassifications corresponding to responses measured by differentmodality sensors (e.g., two or more different sensors). For example, alow GSR response, a low pupil dilation, a low negative FAC, and a lowpositive FAC may be combined to indicate a mental classification of lowand/or no engagement (boredom). In some examples, a second mentalclassification is determined based on additional responses measuredduring a second time frame. A mental state is designated if consecutivemental classifications are similar (e.g., the mental state is designatedbased on a degree of similarity between consecutive mentalclassifications). In some examples, the mental state indicates areaction of the individual to the presented content (e.g., frustration,confusion, etc.). In such examples, the content is adjusted or newcontent (e.g., second content), different from the content originallypresented (e.g., first content), is presented to the individual toincrease the positivity level of the reaction.

In the examples disclosed herein, the responses are measured duringoverlapping time frames to ensure that no peak measurements (e.g., apeak galvanic skin response (GSR) measurement) are missed. For example,the first time frame begins when the content is presented to theindividual and the second time frame begins prior to an end of the firsttime frame. In some such examples, each time frame has a duration offour seconds and the second time frame begins one second after the firsttime frame begins. In conventional methods in which discrete windows areused, a peak that occurs at the end of one window and into the next maybe lost.

In some examples, the response classifications are combined in timesegments that include multiple overlapping time frames. For example, ifeach time frame has a duration of four seconds, the time segments mayhave a duration of two seconds. In such examples, the time segmentsinclude responses from up to four different time frames. In someexamples, a mental classification is determined for each time segmentbased on the response classification corresponding to the respectivetime segments. In some such examples, the mental state is designated ifthe mental classifications for consecutive time segments (e.g., two ormore consecutive segments) are similar. Alternatively, no mental stateis designated if no consecutive mental classifications are similar.

Disclosed in some examples herein, are methods to adjust contentpresented to an individual. The example method includes measuring, via afirst modality sensor, a first response of the individual to firstcontent during a first time frame and determining a first responseclassification based on a first comparison of the first response and afirst threshold. The example method also includes measuring, via asecond modality sensor, a second response of the individual to the firstcontent during the first time frame and determining a second responseclassification based on a second comparison of the second response to asecond threshold. In addition, the example method includes determining afirst mental classification of the individual based on combining thefirst response classification and the second response classification anddetermining a first baseline during the first time frame, at least oneof the first threshold or second threshold based on the first baseline.The example method includes measuring, via the first modality sensor, athird response of the individual to first content during a second timeframe and measuring, via the second modality sensor, a fourth responseof the individual to the first content during the second time frame. Inaddition, the method includes adjusting the first baseline to a secondbaseline based on at least one of the third response or the fourthresponse in the second time frame, adjusting at least one of the firstthreshold to a third threshold or second threshold to a fourth thresholdbased on the second baseline, determining a third responseclassification based on a third comparison of the third response and thethird threshold, and determining a fourth response classification basedon a fourth comparison of the fourth response and the fourth threshold;determining a second mental classification of the individual based oncombining the third response classification and the fourth responseclassification. Other aspects of the example method include determininga mental state of a user based on a degree of similarity between thefirst mental classification and the second mental classification, and atleast one of modifying the first content to include second content orreplacing the first content with second content based on the mentalstate.

In some examples, the first modality sensor includes a galvanic skinresponse sensor. Also, in some examples, the second modality sensorincludes a pupil dilation sensor.

In some examples, the method also includes generating a cognitive loadindex based on data from the pupil dilation sensor. The cognitive loadindex is representative of how much of a maximum information processingcapacity of the individual is being used.

In some example methods, the second time frame partially overlaps thefirst time frame.

In some examples, the method includes measuring, via a third modalitysensor, a fifth response of the individual to the first content duringthe first time frame and determining a fifth response classificationbased on a fifth comparison of the fifth response and a fifth threshold,the first mental classification of the individual based on combining thefirst response classification and the second response classificationfurther with the fifth response classification. The method also includesmeasuring, via the third modality sensor, a sixth response of theindividual to the first content during the second time frame, anddetermining a sixth response classification based on a sixth comparisonof the sixth response and the fifth threshold, the second mentalclassification of the individual based on combining the third responseclassification and the fourth response classification further with thesixth response classification.

In some examples, the third modality sensor includes a facial actioncoding sensor. Also, in some examples, the third modality sensorincludes an eye tracking sensor.

Also, in some examples disclosed herein, the second content is toincrease a positivity level of the mental state. In addition, in someexamples, the second content is to at least one of induce a purchase orincrease a total spend amount on a purchase.

Also disclosed herein are example systems including, a system thatincludes a first modality sensor, a second modality sensor, and aprocessor. In the example system, the processor is to measure, via thefirst modality sensor, a first response of an individual to firstcontent during a first time frame and determine a first responseclassification based on a first comparison of the first response and afirst threshold. The example processor also is to measure, via thesecond modality sensor, a second response of the individual to the firstcontent during the first time frame, and determine a second responseclassification based on a second comparison of the second response to asecond threshold. The example system also uses the processor todetermine a first baseline during the first time frame, at least one ofthe first threshold or second threshold based on the first baseline anddetermine a first mental classification of the individual based oncombining the first response classification and the second responseclassification. In addition, the processor is to measure, via the firstmodality sensor, a third response of the individual to first contentduring a second time frame, measure, via the second modality sensor, afourth response of the individual to the first content during the secondtime frame, adjust the first baseline to a second baseline based on atleast one of the third response or the fourth response in the secondtime frame, and adjust at least one of the first threshold to a thirdthreshold or the second threshold to a fourth threshold based on thesecond baseline. Other determinations are also made by the exampleprocessor including, for examples, determining a third responseclassification based on a third comparison of the third response and thethird threshold, determining a fourth response classification based on afourth comparison of the fourth response to the fourth threshold,determining a second mental classification of the individual based oncombining the third response classification and the fourth responseclassification, and determining a mental state of a user based on adegree of similarity between the first mental classification and thesecond mental classification. In addition, the example system uses theprocessor to at least one of modify the first content to include secondcontent or replace the first content with second content based on themental state.

Also disclosed herein are tangible computer readable storage mediacomprising instructions that, when executed, causes a processor of acontent presentation device to at least measure, via a first modalitysensor, a first response of an individual to first content during afirst time frame, determine a first response classification based on afirst comparison of the first response and a first threshold, measure,via a second modality sensor, a second response of the individual to thefirst content during the first time frame, and determine a secondresponse classification based on a second comparison of the secondresponse to a second threshold. In these examples, the instructionsfurther cause the machine to determine a first baseline during the firsttime frame, at least one of the first threshold or second thresholdbased on the first baseline, and determine a first mental classificationof the individual based on combining the first response classificationand the second response classification. In addition, executing theinstructions also causes the machine to measure, via the first modalitysensor, a third response of the individual to first content during asecond time frame, measure, via the second modality sensor, a fourthresponse of the individual to the first content during the second timeframe, adjust the first baseline to a second baseline based on the thirdresponse or the fourth response in the second time frame, and adjust atleast one of the first threshold to a third threshold or the secondthreshold to a fourth threshold based on the second baselines.Furthermore, in this example, the machine is caused by the executedinstructions to determine a third response classification based on athird comparison of the third response and the third threshold,determine a fourth response classification based on a fourth comparisonof the fourth response to the fourth threshold, determine a secondmental classification of the individual based on combining the thirdresponse classification and the fourth response classification, anddetermine a mental state of a user based on a degree of similaritybetween the first mental classification and the second mentalclassification. Also, in this example, the machine is to at least one ofmodify the first content to include second content or replace the firstcontent with second content based on the mental state.

Further disclosed herein are systems such as an example system thatincludes a first modality sensor to measure a first response of anindividual to first content during a first time frame. The examplesystem also includes a second modality sensor to measure a secondresponse of the individual to the first content during the first timeframe. The first modality sensor is to measure a third response of theindividual to first content during a second time frame, and the secondmodality sensor is to measure a fourth response of the individual to thefirst content during the second time frame. The example system includesa response classifier to determine a first response classification basedon a first comparison of the first response and a first threshold anddetermine a second response classification based on a second comparisonof the second response to a second threshold. In addition, the examplesystem includes a baseline generator to determine a first baselineduring the first time frame, at least one of the first threshold orsecond threshold based on the first baseline and adjust the firstbaseline to a second baseline based on at least one of the thirdresponse or the fourth response in the second time frame. The baselinegenerator also is to adjust at least one of the first threshold to athird threshold or the second threshold to a fourth threshold based onthe second baseline. In addition, the response classifier is to furtherdetermine a third response classification based on a third comparison ofthe third response and the third threshold and determine a fourthresponse classification based on a fourth comparison of the fourthresponse to the fourth threshold. The example system also includes amental classifier to determine a first mental classification of theindividual based on combining the first response classification and thesecond response classification and determine a second mentalclassification of the individual based on combining the third responseclassification and the fourth response classification. The mentalclassifier also is to determine a mental state of a user based on adegree of similarity between the first mental classification and thesecond mental classification. The example system also includes a contentmodifier to at least one of modify the first content to include secondcontent or replace the first content with second content based on themental state.

In some examples, the system further includes a third modality sensor tomeasure a fifth response of the individual to the first content duringthe first time frame and measure a sixth response of the individual tothe first content during the second time frame. Also, in such examplesystems, the response classifier is to determine a fifth responseclassification based on a fifth comparison of the fifth response and afifth threshold, and the mental classifier is to base the first mentalclassification of the individual on combining the first responseclassification and the second response classification further with thefifth response classification. In addition, the response classifier isto determine a sixth response classification based on a sixth comparisonof the sixth response and the fifth threshold, and the mental classifieris to base the second mental classification of the individual oncombining the third response classification and the fourth responseclassification further with the sixth response classification.

Turning now to the figures, FIG. 1 is a schematic illustration of anexample system 100 in which an example content presentation device 102is implemented. In some examples, the example system 100 is implementedin a laboratory environment for monitoring an individual 104. In otherexamples, the system 100 may be implemented in other environmentsincluding, for example, a public location or a private residence.

In the illustrated example system 100, the content presentation device102 is a desktop computer. In other examples, the content presentationdevice 102 may be any device suitable to present media content to anindividual 104, such as a television, a radio, an Internet-streamedaudio source, a workstation, a kiosk, a laptop computer, a tabletcomputer, an e-reader, a smartphone, etc. The example contentpresentation device 102 presents media content to the individual 104that includes audio, visual, and/or audio-visual content. In someexamples, the content is advertisement(s) and/or entertainment. Also, insome examples, the content is interactive, such as a video game, liveinteraction, or an Internet experience (e.g., a website). The examplecontent presentation device 102 includes a display 106 and/or an audiooutput 108 (e.g., speakers, a headset) to present the media content tothe individual 104. In some examples, the display 106 and/or the audiooutput 108 enables the individual 104 to interact with the contentpresentation device 102. The content presentation device 102 includesone or more of a keyboard 110, a mouse 112, a touchscreen, a microphone,a remote control, etc. to facilitate an interaction between theindividual 104 and the content presentation device 102.

In some examples, the content presentation device 102 is used to measureand/or record self-reported responses, such as responses to computergenerated surveys, text input, and/or audio responses. Self-reportedmeasurements include, but are not limited to, survey responses to itemssuch as perception of the experience, perception of the usability orlikeability of the experience, level of personal relevance to user,attitude toward content or advertising embedded in the content, intentto purchase a product, game, or service, and changes in responses frombefore and after testing.

In some examples, the input devices (e.g. the mouse 112 and/or keyboard110 and/or other input devices) include sensors (e.g., biometricsensors, pressure sensors) to measure a response of the individual 104.For example, interactive content is presented to the individual 104according to a predefined program or sequence biometric response data isrecorded and synchronized or mapped to the content presentation toindicate what biological response the individual 104 had to what portionof the presentation.

As shown in FIG. 1, the additional input devices of the example system100 include one or more modality sensors, such as sensors 114, 116, 118,to monitor a reaction of the individual 104 to the presented content. Insome examples, the modality sensors 114, 116, 118 are communicativelycoupled to the example content presentation device 102. Alternatively,one or more of the modality sensors 114, 116, 118 are integrated withthe example content presentation device 102. The modality sensorsinclude one or more of a camera 114 and/or biometric sensors, such asbiometric sensing clothing 116 and a biometric sensing bracelet 118. Insome examples, the camera 114 is a video camera and/or an infraredcamera. The modality sensors 114, 116, 118 are operative to measure, forexample, any combination of eye tracking responses, behavioralresponses, and/or other biological responses. In some examples, eyetracking responses include pupil dilation, saccadic motion, gazelocation (e.g., direction of attention) and duration, and iris size. Insome examples, behavioral responses include facial expressions, levelsof vocalized emotion (e.g., measure of stress of the individual 104) andmovement. The detected facial expressions express discrete emotions onthe face, such as joy, surprise, confusion, sadness, etc. In someexamples, the biological responses include heart rate (HR) (e.g.,camera-based heart rate measurement), galvanic skin response (GSR) (ameasure of skin conductivity), neural activity (EEG), respiration, bloodflow in the prefrontal cortex (detected by near infrared spectroscopy),activity in specific brain areas (e.g., as determined by functionalMRI), skin temperature, other body temperatures, blood pressure, EMG,etc.

In some examples, the measured response data is linked and/orsynchronized with the content presentation using time stamps and/orevent windows. For example, the presentation is divided into eventwindows based on specific tasks or activities that are included in theinteractive content presented to the individual 104, and the measuredresponse data is associated with the event windows based on the tasks oractivities. In some examples, each task or activity has one or moreevent windows associated with the task or activity. Additionally, eachevent window can be the same or a different duration of time as theother event windows.

The one or more modality sensors 114, 116, 118 and/or the contentpresentation device 102 are in communication with a server 120 via awired or wireless network 122. In some examples, the sensors 114, 116,118 are coupled to the network 122 via the content presentation device102. In some examples, the network 122 uses communication technologiessuch as RS-232, Ethernet, Wi-Fi, Bluetooth or ZigBee. The server 120additionally is in communication with a results analyzing device 124,which is illustrated as a desktop computer but other devices may be usedas noted herein. Additionally or alternatively, more than onecommunication technology is used at the same time, including wiredcomponents (e.g., Ethernet, digital cable, etc.) and wireless components(Wi-Fi, WiMAX, Bluetooth, etc.) to connect the sensors 114, 116, 118and/or other computer system components to the server 120.

Alternatively or additionally, the results analyzing device 124 includesany device suitable to analyze data collected by the contentpresentation device 102 and/or the modality sensors 114, 116, 118,including a workstation, a kiosk, a laptop computer, a tablet computer,and a smartphone. In some examples, the results analyzing device 124receives input from a reviewer 126 related to the results correspondingto the individual 104. The results are transmitted to, for example, theserver 120, a second server, and/or an additional computing device.Alternatively, the results include a generated report 128 (e.g., a hardor a soft copy) distributed to, for example, a client. In some examples,the results analyzing device 124 is integrated with the contentpresentation device 102 to determine moment-to-moment, event-to-event ortotal level of emotion and cognition classifiers and provides theresults to the server 120. Analyzing the results using the resultsanalyzing device 124 prior to transmitting the results the server 120decreases the amount of data transferred, resulting in faster dataprocessing and lower transmission bandwidth requirements to increase theoperating efficiency of the system.

As used herein, the phrase “in communication,” including variancesthereof, encompasses direct communication and/or indirect communicationthrough one or more intermediary components and does not require directphysical (e.g., wired) communication and/or constant communication, butrather additionally includes selective communication at periodic oraperiodic intervals, as well as one-time events.

FIG. 2 is a block diagram of an example system 200 that may be used toimplement one or more of the example devices of the example system 100of FIG. 1. The example system 200 includes a mental state determinationmodule 202. The example mental state determination module 202 determinesa mental state of the individual 104 based on measured responses tocontent presented to the individual 104.

The example mental state determination module 202 is communicativelycoupled to a plurality of modality sensors including, for example, afirst modality sensor 204, a second modality sensor 206, and an Nthmodality sensor 208. In some examples, the first modality sensor 204,the second modality sensor 206, and the Nth modality sensor 208correspond to any of the camera 114, the biometric sensing clothing 116,and the biometric bracelet 118 of FIG. 1. Alternatively, the first,second, and Nth modality sensors 204, 206, and 208 include any sensoroperative to measure at least one of an eye tracking response, abehavioral response, or a biometric response of the individual 104.Additional modality sensors may be included to measure other biometricresponses of the individual.

The example mental state determination module 202 includes aninput/output interface (I/O interface) 210. The example I/O interface210 is operatively coupled to the first, second, and Nth modalitysensors 204, 206, and 208 to communicate a response of the individual104 to the example mental state determination module 202. Additionallyor alternatively, the I/O interface 210 is operatively coupled to any ofthe display 106, the audio output 108, the keyboard 110, the mouse 112,a touchscreen, a microphone, or any other device capable of providing anoutput to the individual 104 and/or providing an input to the contentpresentation device 102. Additionally, the I/O interface 210 is incommunication with the server 120 of FIG. 1 to communicate with theexample results analyzing device 124 and/or any other device incommunication with the server 120. For example, the I/O interface 210receives the content to be presented to the individual 104 viacommunication with the server 120. Alternatively or additionally, theI/O interface 210 transmits results from the content presentation device102 to the server 120.

In the illustrated example, the mental state determination module 202includes storage 212 (e.g., a mass storage device) to store responsedata corresponding to the individual 104, content to be presented to theindividual 104, and/or instructions for processing the response data.The example storage 212 is in communication with the I/O interface 210to send and receive response data and/or media content to and/or from,for example, the first modality sensor 204, the second modality sensor206, the Nth modality sensor 208, and/or the server 120. Alternativelyor additionally, in some examples, the example storage 212 is in directcommunication with one or more of the first modality sensor 204, thesecond modality sensor 206, the Nth modality sensor 208, and the server120.

The example mental state determination module 202 includes a responseclassifier 214 to determine a response classification of measuredresponse data received from one or more of the first, second, and Nthmodality sensors 204, 206, and 208. The example response classifier 214determines the response classification (e.g., high or low heart rate;high or low GSR; high, medium, or low pupil dilation; etc.) by, forexample, comparing the measured response to a threshold. In someexamples, each measured response corresponding to one of the modalitysensors 204, 20, 208 is compared to a different threshold (e.g., arespective threshold) based on, for example, different biologicalcharacteristics of the signals and responses for the respectivemodality. The threshold is determined based on, for example, an averagevalue of the measured response during an initial time period (e.g., abaseline). In some examples, the response classifier 214 determineswhich responses are most likely to be relevant to the mental state ofthe individual 104 from the available response measurements. In someexamples, the selection of responses relevant to the mental state isconfirmed using a research methodology. For example, a hypothesis isgenerated, a study is created, participants are recruited, data iscollected and analyzed, and a conclusion is drawn. Additionally oralternatively, in some examples, a statistical model of thecontributions of each of the responses is created to select theresponses with the greatest relevance to the mental state of theindividual 104. The example statistical model may be used to classifyresponses and/or determine a range for characterization of responsesusing an assumed statistical probability density. In some examples, thestatistical model may form the bases for classification barriers anddetermine if one mental state is more likely than another.

The example mental state determination module 202 includes an examplebin classifier 216. In some examples, the bin classifier 216 creates oneor more bins in which to place each response based on the respectivethresholds and/or a baseline. For example, each response (e.g., themeasurement from each modality sensor 204, 206, 208) is sorted into abin (e.g., a high bin, a low bin) based on the comparison to thethreshold, and the one or more bins may be created based on thebaseline. In some examples, the bin classifier 216 determines binningcriteria based on the response measurement and/or sensor measuring theresponse being binned. For example, for GSR binning, the baseline (e.g.,the binning criteria) is determined by calculating a mean GSR for aportion of the response measurement. Example GSR bins include a high bin(e.g., 50% increase above the mean GSR) and a low bin (e.g., 25%increase above the mean GSR). Additionally or alternatively, binningcriteria for an HR response measurement is determined by a change inabsolute beats-per-minute (bpm) within a two-second window. Example HRbins include a high bin (e.g., increase of 12-15 bpm) and a low bin(e.g., decrease of 12-15 bpm).

Additionally or alternatively, the bin classifier 216 divides someresponses, such as facial responses, into positive and negativecategories prior to sorting the response into a high or low bin. In suchexamples, a database of facial responses is created from participantsduring testing to determine relative baseline(s) for positive andnegative expressions. Example facial response bins include a high bin(e.g., one standard deviation in probability of coding an expression aspositive/negative above the standard deviation and the opposite responseis low (i.e., to code high positive FAC, negative FAC response must below)) and a low bin (e.g., one standard deviation decrease inprobability, below data baseline, of coding an expression aspositive/negative).

In some examples, the bin classifier 216 creates an intermediate bin forresponse measurements, such as pupil dilation. For example, pupildilation binning includes determining the baseline based on a functionof change from mean pupil dilation during some portion of the measuredresponse. In some examples, pupil dilation binning includes a medium binto capture times when the individual 104 is cognitively engaged, but notnecessarily heavily concentrating, frustrated, or bored. Example pupildilation bins include a high bin (e.g., mean dilation plus at least onehalf standard deviation), a medium bin (e.g., within one half standarddeviation of the mean), and a low bin (e.g., mean dilation minus atleast one half standard deviation). In some examples, the responseclassifier 214 uses the bins to determine the response classification ofa response and/or places the responses in bins based on the comparisonof the response to the threshold. In some such examples, the responseclassifier 214 and the bin classifier 216 work cooperatively to placeresponses in an appropriate bin based on the response classificationand/or the comparison of the response to the threshold or baseline.

Typically, all responses are weighted equally when determining themental state of the individual 104 (e.g., if three responses weremeasured, each response contributes to the mental state 33%). In someexamples, the responses are weighted by adjusting the contribution ofone or more responses to the overall results to be more or less than thecontribution of other responses. For example, if one of the modalitysensors 204, 206, 208 is not measuring data for all or part of thecontent presentation, the weighting of the contribution of each responseis adjusted (e.g., if only two responses are measured at a given time,each response contributes to the mental state 50%). As more responsedata is collected and/or as the reaction to the presented contentchanges, the weights of the responses contributions can be adjusted toimprove accuracy of the results (e.g., the response classification, amental classification, the determined mental state).

The example mental state determination module 202 includes an examplebaseline generator 218. In some examples, the baseline generator 218determines an initial baseline using a baselining procedure. Forexample, response measurements are not binned (e.g., classified) for aninitial time period such as, for example, thirty seconds. The length ofthe initial time period may vary based on a task being performed by theindividual. In some examples, neutral content is presented to theindividual 104 during the initial time period. During the initial timeperiod, a representative value (e.g., a mean value, a standarddeviation, etc.) is determined by the baseline generator 218, forexample, for the responses related to each sensor and are used as theinitial baseline. In such examples, responses are compared to theinitial baseline and/or a respective one of the thresholds. Additionallyor alternatively, after the initial time period, the baseline isperiodically adjusted based on the measured responses. For example, thebaseline generator 218 re-evaluates the baseline for each baseline timeperiod (e.g., thirty seconds) and adjusts the baseline based on theresponse. Alternatively or additionally, the baseline time period is thesame as the first time frame. For example, if a user's GSR is above themean (e.g., sorted in the high bin) for a period of time and then dropsbelow the mean, the baseline determiner 218 adjusts the baseline inresponse to the drop in the GSR measurement to establish a new baseline.Thus, the baseline determiner 218 automatically adjusts the baselinecorresponding to each response measurement in response to the occurrenceof relevant events.

Automatically adjusting the baseline as the response of the individualchanges and/or develops increases the accuracy of the determined mentalstate. For example, determining the mental state based on a singleand/or constant baseline may not detect fluctuations or changes in theresponse of the individual (e.g., drop in heart rate after a period ofhigher heart rate) because the response (e.g., heart rate) may still behigher than the initial baseline and, thus, classified as high. Thefailure to detect these fluctuations or changes may result in anincorrect classification of the response.

In addition, there are many advantages to adjusting the baseline. Forexample, if an individual is experiencing frustration with a website,which is detected based on the individual's GSR being above a thresholdrelative to the baseline, and there may be a modification of the contentto alter the mental state of the user to a more enjoyable experience.The modification of the content may begin to work to bring theindividual to a less frustrated state. However, at the initial stages ofthe change, the individual's GSR may remain above the threshold relativeto the baseline, though the individual's mental state is changing inaccordance with the goals of the modified content. However, thesechanges may go undetected based on the level of GSR compared to thethreshold relative to the baseline. Whereas, an adjusted baseline wouldchange the threshold trigger, and enable detection of the GSR (in thisexample) moving across the threshold and provide indication that thecontent modification is effective. Therefore, the content modificationcan continue to bring the individual into the desired mental state. Inaddition, where the baseline is moved and the threshold has not beentriggered though content has been modified, the operator or websiteowner would know that the content modification did not work (or did notwork fast enough) or that a secondary baseline adjustment may be neededfor a finer detection of biometric responses and/or mentalclassification and state changes.

In some examples, a running window implementation is used. In some suchexamples, the running windows include overlapping time windows (e.g.,four-second windows). In some examples, the responses are measured usingoverlapping windows to avoid inaccuracies and/or missed events in thecollected data. For example, GSR measurements typically peak atapproximately four to five seconds, which can be missed ormisinterpreted using non-overlapping time windows to measure GSRresponses. In the example disclosed herein, the time windows each have aduration of four seconds and begin in one-second increments. In otherexamples, any other suitable or desired time duration(s) and/orincrement(s) may be used. In some examples, the response is binnedand/or a response classification is determined for each of the timewindows.

The example mental state determination module 202 also includes anexample mental classifier 220. The example mental classifier 220determines mental classifications related to the measured responses(e.g., raw data from the modality sensors) and/or the responseclassifications (e.g., response data that is classified based on athreshold). In some examples, the mental classifications are determinedby combining response classifications (e.g., high heart rate and lowGSR) corresponding to one or more modality sensors 204, 206, and 208. Inthe illustrated example, the response classifications are combined intime segments shorter than the time windows (e.g., two seconds) andinclude the response classifications determined for each time windowrelated to the time segment.

In some examples, the mental classifier 220 uses a mental classificationgrid, such as the example mental classification grid 300 in FIG. 3, todetermine a mental classification for a time segment based on the one ormore response classifications associated with the time segment. FIG. 3is a representation of an example mental classification grid 300 thatmay be used by the example systems of FIGS. 1 and/or 2 to adjust contentpresented to an individual 104. In the illustrated example, the mentalclassification grid 300 includes three axes (e.g., cognitive load 302,emotional valence 304, and emotional arousal 306). In other examples,the classification grid 300 includes more than three axes (e.g., fouraxes, five axes, etc.).

In some examples, one of the axes used to determine a mentalclassification of an individual is cognitive load. The cognitive loadaxis 302 refines the classifications and/or the emotional valence 304and the emotional arousal 306. The cognitive load is determined based onbiological measures, such as measurements of pupil dilation. A cognitiveload index represents the maximum amount of information the individual104 can process at a given time. Cognitive load 302 is quantified basedon the index to represent how much information the individual 104 isprocessing at a given time. Including cognitive load 302 as an axis inthe example mental classification grid 300 provides significantfunctionality. Each individual 104 is determined to have a maximuminformation processing capacity. Comparing the cognitive load of theindividual 104 during a period of time to the cognitive load indexprovides information related to the mental state of the individual 104.For example, if the individual 104 exhibits a high cognitive load indexand a low emotional index, the determined mental state is concentration.In some examples, the emotional index is based on the emotional valence304 and/or the emotional arousal 306. Thus, the use of cognitive load302 allows the example mental state determination module 202 and/or themental classifier 220 to distinguish between mental states such asfrustration, confusion, and concentration.

Additionally or alternatively, the mental classifier 220 uses a mentalclassification matrix, such as the example mental classification matrix400 of FIG. 4, to determine a mental classification based on one or moreresponse classifications. FIG. 4 is a representation of an examplemental classification matrix 400 that is used by the example system ofFIGS. 1 and/or 2 to adjust content presented to an individual 104. Theexample classification matrix 400 represents an example method ofcombining example response classifications 402 to determine examplemental classifications 404. In the illustrated example mentalclassification matrix 400, the example response classifications 402represent possible combinations of response classifications for aresponse measurement. For example, a first response measurement mayoccur during a first time window (e.g., time window 502 of FIG. 5) andcorrespond to high GSR, high positive FAC, and low negative FAC, whichare combined by, for example, the example mental classifier 220 toproduce the mental classification of active engagement (positive). Othermental classifications are created by combining different responseclassifications 402 for similar response measurements.

In the illustrated example, response classifications 402 that can becombined by the mental classifier 220 to create other mentalclassifications 404 (e.g., active engagement (frustration), activeconcentration (flow state), passive concentration, low/no engagement(boredom), etc.) include one or more response classifications 402different than the response classifications 402 combined to provide anactive engagement (positive) mental classification 404. The exampleresponse classifications 402 in the example mental classificationsmatrix 400 include response classifications 402 corresponding tomeasurements (e.g., GSR, pupil dilation, FAC, etc.) using the sensors(e.g., the first modality sensor 204, the second modality sensor 206,the Nth modality sensor 208, the camera 114, the biometric sensingclothing 116, the biometric sensing bracelet 118, etc.). In otherexamples, the example response classifications 402 correspond toadditional and/or alternative sensor measurements (e.g., HR, EEG, pupiltracking, etc.). The example mental classification matrix 400illustrated in FIG. 4 represents only a portion of the potentialresponse classifications 402 and/or mental classifications 404 that canbe designated as the mental state of the individual and, thus, themental state and/or the mental classifications 404 are not limited tothe example mental classifications 404 in the mental classificationmatrix 400 of FIG. 4.

FIG. 5 is a representation of overlapping time windows 502-510 used bythe example system of FIGS. 1 and/or 2 to adjust content presented to anindividual 104. The example time windows 502-510 correspond to responsemeasurements from a single modality sensor such as, for example thefirst modality sensor 204. In some examples, the first time window 502begins when the content is presented to the individual 104. In someexamples, each of the subsequent time windows 504-510 begin one secondafter the previous time window (e.g., each time window 504-510 begins ina one-second interval). For example, the first time window 502 begins attime 0:00:00, as shown by the timeline 512, a second time window 504begins at time 0:00:01, and a third time window 506 begins at time0:00:02. Alternatively, the time windows 502-510 begin at differentintervals determined based on, for example, the length of each of thetime windows 502-510 and/or the length of each of the segments 514-520.In some examples, the response measurements from the other sensors(e.g., the second modality sensor 206, the Nth modality sensor 208,etc.) have time windows of the same duration as the length of the timewindows 502-510. Additionally or alternatively, the time windows 502-510correspond to a response classification for the response measurement.For example, the example response classifier 214 determines a responseclassification for the response measurements in each of the time windows502-510.

In some examples, the example response classifications corresponding totime windows 502-510 are combined in two-second time segments 514-520 todetermine a mental classification for each of the time segments 514-520.In some examples, the response measurements from all modality sensors204, 206, 208 are combined during the same time segment (e.g., timesegment 514) to determine a mental classification corresponding to thetime segment 514. Additionally or alternatively, the time windows502-510 falling within each of the time segments 514-520 are combined todetermine the mental classification for a time segment (e.g., timesegment 516). For example, the mental classification corresponding totime segment 516 is determined by combining response classificationsfrom all time windows (e.g., the first four time windows 502-508) thatat least partially overlap and/or fall within the time segment 516. Inthe illustrated example of FIG. 5, the time segments 514-520 includefour different mental classifications (e.g., positive active, negativeactive, positive passive, and negative passive) and no mental state ofthe individual 104 is designated. Alternatively, one or more consecutivetime segments include a similar mental classification (e.g., segments514-516 are positive active) and a mental state of the individual 104 isdesignated based on the similar mental classification.

In some examples, the mental classifier 220 (FIG. 2) selects relevantresponses (e.g., response measurements likely to be indicative of themental state of the individual 104) used to determine the mentalclassification of the individual 104. In some examples, additionalresponses of the individual 104 are measured for the first time frameduring which content is presented to the individual 104. In suchexamples, an additional response classification is determined based on acomparison of the additional response measured to an additionalthreshold. In some such examples, the additional response classificationis combined with the first response classification. In such examples,the first mental classification is altered based on the additionalresponse classification.

After the mental state determination module 202 determines the mentalstate of the individual 104, an example content modifier 222 determineswhether to modify the content. In some examples, the content modifier222 edits the content presented to the individual 104 based on thedetermined mental state of the individual 104. For example, if themental state indicates that the individual 104 is frustrated, thecontent modifier 222 presents new content (e.g., second content) to theindividual and/or adjusts the content to increase a positivity of themental state. In some examples, the new content includes a coupon and/ora video (e.g., a tutorial video). Alternatively, the new content is acoupon, a free gift, a suggestion, etc. In some such examples, the newcontent induces a purchase of a product. In other examples, the newcontent increases a total amount spent on a purchase.

In some examples, the new content is provided as an output 224, such ascontent displayed via the display 106, printed content, audio content,or any other type of media content presentable to the individual 104. Insome examples, the output 224 includes response data (e.g., responseclassifications, mental classifications, and the determined mentalstate) transmitted to the server 120. In some examples, the output 224is in communication with the server 120 and/or the content presentationdevice 102 via the I/O interface 210 of the mental state determinationmodule 202.

While an example manner of implementing the system 100 of FIG. 1 isillustrated in FIG. 2, one or more of the elements, processes and/ordevices illustrated in FIG. 2 may be combined, divided, re-arranged,omitted, eliminated and/or implemented in any other way. Further, theexample mental state determination module 202, the example firstmodality sensor 204, the example second modality sensor 206, the exampleNth modality sensor 208, the example I/O interface 210, the examplestorage 212, the example response classifier 214, the example binclassifier 216, the example baseline generator 218, the example mentalclassifier 220, the example content modifier 222, the example output224, and/or, more generally, the example systems 100, 200 of FIGS. 1 and2 may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example mental state determination module 202, the examplefirst modality sensor 204, the example second modality sensor 206, theexample Nth modality sensor 208, the example I/O interface 210, theexample storage 212, the example response classifier 214, the examplebin classifier 216, the example baseline generator 218, the examplemental classifier 220, the example content modifier 222, the exampleoutput 224 and/or, more generally, the example systems 100, 200 of FIGS.1 and 2 could be implemented by one or more analog or digitalcircuit(s), logic circuits, programmable processor(s), applicationspecific integrated circuit(s) (ASIC(s)), programmable logic device(s)(PLD(s)) and/or field programmable logic device(s) (FPLD(s)). Whenreading any of the apparatus or system claims of this patent to cover apurely software and/or firmware implementation, at least one of theexample mental state determination module 202, the example firstmodality sensor 204, the example second modality sensor 206, the exampleNth modality sensor 208, the example I/O interface 210, the examplestorage 212, the example response classifier 214, the example binclassifier 216, the example baseline generator 218, the example mentalclassifier 220, the example content modifier 222, the example output 224and/or, more generally, the example systems 100, 200 is/are herebyexpressly defined to include a tangible computer readable storage deviceor storage disk such as a memory, a digital versatile disk (DVD), acompact disk (CD), a Blu-ray disk, etc. storing the software and/orfirmware. Further still, the example systems 100, 200 of FIGS. 1 and 2may include one or more elements, processes and/or devices in additionto, or instead of, those illustrated in FIG. 2, and/or may include morethan one of any or all of the illustrated elements, processes anddevices.

A flowchart representative of example machine readable instructions forimplementing the systems 100, 200 of FIGS. 1 and 2 is shown in FIGS. 6and 7. In this example, the machine readable instructions comprise aprogram for execution by a processor such as the processor 812 shown inthe example processor platform 800 discussed below in connection withFIG. 8. The program may be embodied in software stored on a tangiblecomputer readable storage medium such as a CD-ROM, a floppy disk, a harddrive, a digital versatile disk (DVD), a Blu-ray disk, or a memoryassociated with the processor 812, but the entire program and/or partsthereof could alternatively be executed by a device other than theprocessor 812 and/or embodied in firmware or dedicated hardware.Further, although the example program is described with reference to theflowchart illustrated in FIGS. 6 and 7, many other methods ofimplementing the example systems 100, 200 may alternatively be used. Forexample, the order of execution of the blocks may be changed, and/orsome of the blocks described may be changed, eliminated, or combined.

As mentioned above, the example processes of FIGS. 6 and 7 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a tangible computer readable storagemedium such as a hard disk drive, a flash memory, a read-only memory(ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and transmission media. As usedherein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIGS. 6 and 7 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and transmission media. As used herein, whenthe phrase “at least” is used as the transition term in a preamble of aclaim, it is open-ended in the same manner as the term “comprising” isopen ended.

FIG. 6 is a flowchart representative of example machine-readableinstructions 600 that may be execute for adjusting content presented toan individual 104 (using, for example, the example systems of FIGS. 1and/or 2). The example instructions 600 begin execution when content,including audio, visual, audio-visual, and interactive content, ispresented to the individual 104 via the content presentation device 102(block 602). In an example implementation, one or more individuals ispresented, via, for example, the content presentation device 102, awebsite including an interactive configurator. Consider, for example,the individuals using an online configurator for Adagio teas, whichallows users to utilize a web interface to create custom tea blends.Alternatively, other interactive configurators could be used (e.g.,Motorola MotoX Motomaker to custom design a mobile telephone, Mini autoconfigurator to customize an automobile, etc.). The example may also beimplemented with other types of media not restricted to media intendedfor the purchase of customized products such as, for example, shoppingfor off the shelf products, gaming, entertainment, news, education, etc.

The example instructions 600 include measuring biometric and/orneurophysiological responses to content (block 604). For example, one ormore of the example sensors (e.g., the camera 114, the biometric sensingclothing 116, the biometric sensing bracelet 118, and/or the first,second, and Nth modality sensors 204, 206, 208) measures the response ofthe individual 104 to the content, which may include a biometricresponse, a neurophysiological response, and/or a behavioral response.In some examples, a first response to first content is measured by thefirst modality sensor 204 during a first time frame and a secondresponse to first content is measured by the second modality sensor 206during the first time frame. Additionally, in some examples, the firstmodality sensor 204 measures a third response of the individual to firstcontent during a second time frame and the second modality sensor 206measures a fourth response of the individual to the first content duringa second time frame. In the example implementation, sensors collecteddata related to GSR, FAC, eye tracking and pupil dilation for theindividuals exposed to the Adagio tea configurator. The responses areaveraged over a time period (block 606) using, for example, the responseclassifier 214 to average the responses (e.g., heart rate, pupildilation, GSR, etc.) of each sensor 204, 206, 208 over a first timeframe.

For example, the responses of each of the individuals were monitored forthe duration of the exposure to various images, text, displays, and/orother options presented via the Adagio tea configurator. The durationincluded multiple time frames. The responses of the individual wereaveraged for each of the time frames. For example, a sensor detected FACmay detect a furrowed brow and a tight lip during a time window. Thesedetected features could change over the duration of the time window, andan average across the window in determined.

The example instructions 600 include comparing the average of eachresponse to a threshold (block 608). For example, the responseclassifier 214 compares the average response values to respectivethresholds corresponding to the sensors 204, 206, 208 to classify eachresponse (e.g., high heart rate, low heart rate, low GSR, etc.). In someexamples, a first response is compared to a first threshold and a secondresponse is compared to a second threshold. In some examples, the firstthreshold or the second threshold is based on a first baselinedetermined for a first time frame. In some such examples, the firstbaseline is adjusted to a second baseline based on the third responseand/or the fourth response. In some examples, the first threshold isadjusted to a third threshold and/or the second threshold is adjusted toa fourth threshold. Additionally, in some examples, a third response iscompared to a third threshold and a fourth response is compared to afourth threshold.

For example, in the Adagio tea implementation described above, theindividuals using the configurator may be presented with 30 images and 4video clips to establish a baseline and/or threshold to which theresponses measured during the exposure to the Adagio tea configuratorare compared. The individuals' responses from one or more sensors(including for example GSR, FAC, and/or pupillary dilation) are comparedto the thresholds over time. For example, the average FAC response(based on the furrowed brow and tight lip mentioned above) is comparedto a threshold value related to features detected via FACs sensors todetermine a relative level of, for example, furrowed brows and tightlips. In addition, the system operating the configurator may, at times,determine that a baseline and/or threshold may have to be adjusted, asdescribed above and below with respect to FIG. 7.

Each response is placed in a bin based on the comparison (block 610). Inaddition, the example bin classifier 216 places each response into arespective bin (e.g., high, medium, low) based on the comparison to thethreshold. The response classifier 214 determines a responseclassification (e.g., high GSR, low heart rate, low pupil dilation,etc.) for each response based on the comparison of the average responsevalue(s) to the threshold(s) and/or the bin(s) in which each response isplaced.

For example, the responses of the individuals using the Adagio teaconfigurator are placed in high or low GSR bin; high, medium, or low FACbins; and high, medium, or low pupillary dilation bins and/or other binsrelative to the biometric responses detected from the individual whilepresented with the configurator. For example, a low FAC bin may includenegative responses such as, for example, those identifiable by furrowedbrows and tight lips.

The example instructions 600 include assigning a weight to each response(block 612). For example, the response classifier 214 assigns a weightto each response corresponding to the amount each response contributesto the determined mental state of the individual 104. For example, ifthree response classifications are available, each responseclassification may be weighted as 33%. In another exampleimplementation, each of the responses are given a weight correspondingto the contribution of each response to the determined mental state. Forexample, pupil dilation data can be adversely affected due to changinglighting when viewing dynamic media and, thus, the weighting for pupildilation may be less than the weighting for each of GSR, FAC, and/or eyetracking, etc.

The example instructions 600 also determine first responseclassification for each response over a first time period (block 614)and second response classification of each response over a second timeperiod (block 616). For example, the response classifier 214 of FIG. 2may be used to provide these determinations based on the comparison ofthe average response to a threshold (block 608) and/or a bin (block 610)in which the response was placed. In some examples, a first responseclassification is determined based on a first comparison of the firstresponse and a first threshold and a second response classification isdetermined based on a second comparison of the second response to asecond threshold. Additionally, in some examples, a third responseclassification is determined based on a third comparison of the thirdresponse data to a third threshold and a fourth response classificationis determined based on a fourth comparison of the fourth response to afourth threshold.

In the Adagio tea example implementation, responses are measured overnumerous time periods, and response classifications are determined foreach time period by comparing the responses to relevant thresholds todetect fluctuations or changes in the response of the individual. Forexample, based on the comparison of the FACs data (e.g., the furrowedbrows and tight lips) to the thresholds and/or bin data, it may bedetermined that the responses during the measured time periods if low ornegative.

In this example, the instructions 600 combine classifications of eachresponse to create a mental classification (block 618). For example, themental classifier 220 determines a mental classification (e.g.,frustration, confusion, boredom, etc.) for a time segment by combiningresponse classifications from the first time period and/or the secondtime period. In some examples, a first mental classification of theindividual is determined based on combining the first responseclassification and the second response classification. Additionally, insome examples, a second mental classification is determined based oncombining the third response classification and the fourth responseclassification. In the example implementation, an individual withresponse classifications including high GSR, high pupil dilation, highnegative FAC, and low positive FAC was determined to have a mentalclassification of frustrated.

In addition, the example instructions 600 are used to identifyconsecutive similar mental classifications (block 620). For example, themental state determination module 202 of FIG. 2 assess the mentalclassifications and identifies consecutive mental classifications thatare similar (e.g., two time periods during which the mentalclassification is similar). The example instructions 600 use theconsecutive similar mental classification to determine the mental state(e.g., frustration, confusion, boredom, etc.) of the individual (block622) using, for example the mental state determination module 202 ofFIG. 2. In some examples, a mental state of a user is determined basedon a degree of similarity between the first mental classification andthe second mental classification. In the Adagio tea exampleimplementation, an individual having a mental classification offrustrated for at least three consecutive time periods (for example) isassigned a mental state of frustrated.

Based on the determined mental state, the instructions are furtherexecuted to determine whether the mental state should be adjusted (block624) to, for example, increase the positivity, decrease negativity,increase intensity, heighten a concentration and/or otherwise make achange to the mental state. For example, any individual operating theAdagio tea configurator who is experiencing frustration would beidentified as a candidate in need of a mental state adjustment.

If it is determined that the mental state is to be adjusted, theinstructions 600 include modifying the content (block 626). For example,the content modifier 222 of FIG. 2 may be used to adjust the contentpresented to the individual 104. For example, if the mental stateindicates the individual is frustrated, the content modifier may displayadditional content, such as a helpful video, a coupon, quieter or louderaudio, brighter or dimmer display brightness, more or less displayitems, etc. In some examples, the content modifies the first content toinclude second content and/or replaces the first content with secondcontent based on the mental state. For example, the new or modifiedcontent presented to the individual 104 during the tea configurationprocess may also include, for example, a reduced choice of tea flavorsto choose from to help the individual 104 decide, a suggested flavor oftea, a free gift (e.g., a tea infuser), a tutorial video, a suggestedpopular blend, etc. Additionally, in the example implementation, theindividuals are presented with the option to swap the created tea blendfor the tea blend of the month (e.g., a forced choice) and to add onitems, such as cookies and a personalized mug (e.g., upsell) at thecheckout.

After content has been modified (block 626), the control returns toblock 602 and the modified content is presented to the individual andthe example instructions 600 continue with the data gathering andanalysis disclosed above. However, if it is determined that the mentalstate does not need to be adjusted (block 624), the example instructionsare also used to determine whether or not to continue monitoring theindividual (block 628). For example, if the mental state determinationmodule 202 decides to continue monitoring the individual 104, controlreturns to block 604 and monitoring continues. However, if it isdetermined that monitoring is not to continue (block 628), monitoringceases and the process 600 is complete (block 630). In the exampleimplementation, the system continued to monitor the individual throughthe entire experience with the tea configurator and the process wasdesignated as complete after the individual completed the checkoutprocess. The results of the particular example implementation showed apositive impact on the amount of money spent by individuals receivingnew or modified content based on the determined mental state (i.e., theindividuals in the minimal and maximal adaptation groups spent more thanthe individuals in the random and control groups), with the maximaladaptation spending a slightly higher amount.

FIG. 7 is a flowchart representative of example machine-readableinstructions or process 700 that may be executed for adjusting abaseline and/or threshold (using, for example, the example systems ofFIGS. 1 and/or 2). The example instructions 700 include presentingcontent to one of more individuals that does not include a stimulus ortarget material (e.g., neutral or background media) (block 702). Forexample, the example presentation device 102 of FIG. 1 may be used topresent content to an individual including, for example, a websitecontaining a news article, an entertainment video, ambient audio etc.

The example instructions 700 include monitoring biometric responses(block 704). For example, one or more of the example sensors (e.g., thecamera 114, the biometric sensing clothing 116, the biometric sensingbracelet 118, and/or the first, second, and Nth modality sensors 204,206, 208) measures the response of the individual 104 to the content,which may include a biometric response, a neurophysiological response,and/or a behavioral response.

A baseline response is determined (block 706), using for example thebaseline generator 218 if FIG. 2. The baseline may be used to identifybiometric responses of the individual when in an inactive, unengaged, orunstimulated state (e.g., a passive state). The example instructions 700also include establishing a threshold (block 708). The threshold may bedetermined by one of the components of the example system 200 of FIG. 2(e.g., the response classifier 214 and/or baseline generator 218) insome examples, and in other examples, the threshold may be imported intothe example system 200. The threshold values used in the example process700 are set to indicate when a biometric response has deviated from theindividual's baseline by an amount sufficient to signify that a changein a response has occurred.

The example instructions 700 also include presenting the individual withcontent that includes stimulus and/or target material (block 710). Forexample, the presentation device 102 of FIG. 1 maybe used to present awebsite (such as, for example, the tea configurator detailed above) toan individual. Further biometric responses are monitored (block 712) andcompared to the current baseline and/or threshold (block 714).

The example instructions 700 are executed to determine if a period oftime elapses in which the threshold has not been triggered (block 716).Triggering the threshold may mean, for example, meeting a threshold,crossing or exceeding a threshold, falling without or outside of athreshold range, etc. For example, the example system 200 analyzes themonitored biometric responses over time and continually compares theresponses to the threshold, which was established in relation to theindividual's baseline. In some examples, the individual's heart rate maybe monitored to determine if the heart rate moves higher than 10 bmpover a baseline heart rate, or if the heart exceeds an absolute valuechange, or if the heart rate passes 80 bmp, and/or any other suitable ordesired metrics. If the threshold has been triggered within theestablished time period, the individual's responses are continued to bemonitored (block 712).

However, the threshold has not been triggered within the set time period(block 716), the example instructions 700 are executed to determine ifthe content has been modified (block 718) and, if not, content ismodified (block 720), presented to the individual (block 710), andmonitoring continues (block 712). The content may be modified, forexample, in accordance with the example systems 100, 200 of FIGS. 1 and2 and/or the process 600 of FIG. 6.

If the content has been modified (block 718), the example instructions700 adjust the individual's baseline and/or reestablishes the thresholdrelative to the baseline (block 722) using, for example, the examplesystem 200 including the baseline generator 218 as disclosed above. Forexample, if the individual is experiencing frustration and adjustmentsare made the content to change the individual's biometric responses(and, thus, mental state), but the system continues to read theindividual's response as frustrated, there may be an indication that theadjusted content is not sufficient to change the individual's responseto a more positive response.

Additionally or alternatively, this may be an indication that thethreshold set, with respect to the baseline, is insufficient to detect achange in response. For example, if an individual has a baseline heartrate of 75 bpm and a threshold is set to a 5 bpm change (plus or minus),the change may determine when a person is experiencing boredom orfrustration. If the person has a change of 10 bpm, the threshold hasbeen crossed. There may be a desire to present modified content to theindividual to change the response back to a positive response asindicated by the heart rate crossing the threshold back toward the 75bpm baseline. New content may be provided, which alters the individual's10 bmp change to 8 bmp but does not cross the 5 bmp threshold. Thischange indicates that the altered content is effective in changing theresponses to the desired response. However, the change is not enough totrigger threshold and, therefore, goes undetected. This may cause thecontent provider to abandon the content modification, may ultimately beeffective for changing the response to the desired response, or causethe content provider to overcompensate resulting in further andunnecessary modification of the content. With the present examplesystems and processes, the threshold may be adjusted to, for example achange of 2 bmp, for a more fine detection of responses changes. Thisadvancement provides the content with enhanced detection capabilitiesand advanced knowledge of the effectiveness in content modification incauses an individual to have a desired response.

In addition, there are examples in which the baseline itself is to bechanged. For example, if a content provider would like to know when anindividual has a change in heart rate when the measurements alreadyexceed the threshold and/or continuously exceed the threshold for aperiod of time, the baseline is adjusted to reflect the changes in heartrate at levels higher than the previous baseline. In some examples, thebaseline may be changed based on task. A low stress task (e.g., buying atoothbrush) may have a lower baseline for heart rate than a high stresstask (e.g., configuring a car) because the individual is more likelyhave a higher heart rate while performing the higher stress task andwould likely continuously exceed a baseline for a lower stress taskunless the baseline is adjusted according to the task. In some examples,the length of time for which the task is performed affects the change inthe baseline.

FIG. 8 is a block diagram of an example processor platform 800 capableof executing the instructions of FIGS. 6 and 7 to implement theapparatus and systems of FIGS. 1 and 2 including for example, the mentalstate determination module 202, the response classifier 214, the binclassifier 216, the baseline generator 218 and the mental classifier 220and/or content modifier 222 of FIG. 2. The processor platform 800 canbe, for example, a server, a personal computer, a mobile device (e.g., acell phone, a smart phone), a personal digital assistant (PDA), anInternet appliance, a DVD player, a CD player, a digital video recorder,a Blu-ray player, a gaming console, a personal video recorder, a set topbox, or any other type of computing device.

The processor platform 800 of the illustrated example includes aprocessor 812. The processor 812 of the illustrated example is hardware.For example, the processor 812 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer.

The processor 812 of the illustrated example includes a local memory 813(e.g., a cache). The processor 812 of the illustrated example is incommunication with a main memory including a volatile memory 814 and anon-volatile memory 816 via a bus 818. The volatile memory 814 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 816 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 814, 816 is controlledby a memory controller.

The processor platform 800 of the illustrated example also includes aninterface circuit 820. The interface circuit 820 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 822 are connectedto the interface circuit 820. The input device(s) 822 permit(s) a userto enter data and commands into the processor 812. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 824 are also connected to the interfacecircuit 820 of the illustrated example. The output devices 824 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a light emitting diode (LED), a printer and/or speakers).The interface circuit 820 of the illustrated example, thus, typicallyincludes a graphics driver card, a graphics driver chip or a graphicsdriver processor.

The interface circuit 820 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network826 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 800 of the illustrated example also includes oneor more mass storage devices 828 for storing software and/or data.Examples of such mass storage devices 828 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 832 of FIG. 8 may be stored in the mass storagedevice 828, in the volatile memory 814, in the non-volatile memory 816,and/or on a removable tangible computer readable storage medium such asa CD or DVD.

From the foregoing, it will appreciate that the above disclosed methods,apparatus and articles of manufacture are operative to provide theindividual with a better experience when interacting with media content,including websites, by altering content and/or presenting new contentbased on a mental state of the individual while viewing and/orinteracting with the content.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A system comprising: a sensor to measure biometric responses of an individual to media content; and a processor to: determine a first baseline associated with physiology of the individual; determine a first threshold based on the first baseline; perform a first comparison of first biometric responses from the individual measured by the sensor during a first time frame of the media content with the first threshold; when the first biometric responses do not satisfy the first threshold, adjust the first baseline to a second baseline; determine a second threshold based on the second baseline; perform a second comparison of second biometric responses from the individual measured by the sensor during a second time frame of the media content with the second threshold; determine a mental classification of the individual based on the second comparison; and at least one of modify or replace the media content during the second time frame based on the mental classification.
 2. The system of claim 1, wherein the first threshold is an absolute value change from the first baseline, and the second threshold is the absolute value change from the second baseline.
 3. The system of claim 1, wherein the sensor is a heart rate sensor, and the biometric responses include a heart rate of the individual.
 4. The system of claim 3, wherein the first baseline is a resting heart rate of the individual.
 5. The system of claim 1, wherein the processor is to: determine the media content was previously modified; and adjust the first baseline to the second baseline based at least in part on the determination that the media content was previously modified.
 6. The system of claim 1, wherein the mental classification is a first mental classification, and wherein the processor is to: perform a third comparison of third biometric responses from the individual measured by the sensor during a third time frame of the media content with the second threshold; when the third biometric responses do not satisfy the second threshold, adjust the second baseline to a third baseline based on the third biometric responses; determine a third threshold based on the third baseline; perform a fourth comparison of the third biometric responses to the third threshold; and determine a second mental classification of the individual based on the fourth comparison.
 7. The system of claim 1, wherein the first baseline is an average of third biometric responses from the individual measured by the sensor occurring during a third time frame of the media content that is prior to the first time frame.
 8. The system of claim 7, wherein the media content occurring during the third time frame includes neutral or background media.
 9. The system of claim 1, wherein the first time frame is based on a task performed by the individual.
 10. The system of claim 1, wherein the sensor is a first sensor, further including a second sensor, and wherein the processor is to determine the mental classification based at least in part on a third comparison of third biometric responses from the individual measured by the second sensor during the second time frame with a third threshold.
 11. A tangible computer readable storage medium comprising instructions that, when executed, cause a machine to at least: determine a first baseline associated with physiology of an individual exposed to media content; determine a first threshold based on the first baseline; perform a first comparison of a first biometric response of the individual measured by a sensor during a first time frame with the first threshold; when the first biometric response does not satisfy the first threshold, adjust the first baseline to a second baseline; determine a second threshold based on the second baseline; perform a second comparison of a second biometric response of the individual measured by the sensor during a second time frame of the media content with the second threshold; determine a mental state of the individual based on the second comparison; and at least one of modify or replace the media content during the second time frame based on the mental state.
 12. The tangible computer readable storage medium of claim 11, wherein the first threshold is an absolute value change from the first baseline, and the second threshold is the absolute value change from the second baseline.
 13. The tangible computer readable storage medium of claim 11, wherein the sensor is a heart rate sensor, and the first and second biometric responses include a heart rate of the individual.
 14. The tangible computer readable storage medium of claim 13, wherein the first baseline is a resting heart rate of the individual.
 15. The tangible computer readable storage medium of claim 11, wherein the instructions, when executed, cause the machine to: determine the media content was previously modified; and adjust the first baseline to the second baseline based at least in part on the determination that the media content was previously modified.
 16. The tangible computer readable storage medium of claim 11, wherein the mental state is a first mental state, and wherein the instructions, when executed, cause the machine to: perform a third comparison of a third biometric response of the individual measured by the sensor during a third time frame of the media content with the second threshold; when the third biometric response does not satisfy the second threshold, adjust the second baseline to a third baseline based on the third biometric response; determine a third threshold based on the third baseline; perform a fourth comparison of the third biometric response to the third threshold; and determine a second mental classification of the individual based on the fourth comparison.
 17. The tangible computer readable storage medium of claim 11, wherein the first baseline is an average of third biometric responses of the individual occurring during a third time frame of the media content that is prior to the first time frame.
 18. The tangible computer readable storage medium of claim 17, wherein the media content occurring during the third time frame includes neutral or background media.
 19. The tangible computer readable storage medium of claim 11, wherein the first time frame is based on a task performed by the individual.
 20. The tangible computer readable storage medium of claim 11, wherein the sensor is a second sensor, and wherein the instructions, when executed, cause the machine to determine the mental state based at least in part on a third comparison of a third biometric response of the individual measured by a second sensor during the second time frame with a third threshold. 